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Agentic binary reverse engineering for ELF binaries on ARM64, ARMv7, x86_64 - hypothesis-driven analysis with radare2, Ghidra, GDB, QEMU
/plugin install binary-re@2389-research
Full plugin documentation and usage guide
Reverse engineer ELF binaries from embedded devices using radare2, Ghidra, GDB, and QEMU.
/plugin install binary-re@2389-research
binary-re -- structured RE workflow with hypothesis-driven analysisUser: "Analyze this binary from an IoT device - ARM, Linux-based"
Claude: [Triggers binary-re skill]
Let me start with triage to understand what we're working with.
Phase 1: Initial Triage
┌─────────────────────────────────────────┐
│ Architecture: ARM 32-bit LE (v7) │
│ Libc: musl 1.2.3 │
│ Dependencies: libcurl, libssl, libz │
│ Stripped: Yes │
│ Strings: 847 (including URLs, paths) │
└─────────────────────────────────────────┘
FACTS recorded:
- Links against libcurl.so.4 (network client)
- Contains string "api.vendor.com/telemetry"
- Imports socket, connect, SSL_read functions
HYPOTHESIS: This is a telemetry client that phones home over HTTPS
Confidence: 0.7
Evidence: curl+ssl imports, telemetry URL string
Do you want me to proceed with static analysis of network-related
functions, or should we try dynamic analysis under QEMU first?
# Required
sudo apt install radare2 qemu-user gdb-multiarch
# Recommended
# Ghidra from https://ghidra-sre.org/
pip install frida-tools
# ARM sysroots
sudo apt install libc6-armhf-cross libc6-arm64-cross
The LLM drives analysis; the human provides context.
You tell Claude what platform/device the binary came from, what hardware is involved, what the binary is theorized to do, and any constraints (no network, isolated test env, etc).
Claude runs the tools, forms hypotheses from evidence, designs experiments to test theories, and synthesizes findings into something actionable.
The skill asks for confirmation before:
MIT
Get started in seconds
/plugin marketplace add 2389-research/claude-plugins
/plugin install binary-re
Skills auto-trigger when relevant